What the data for this category looks like
The documents involved in cosmetics manufacturing inspection and process record audit include formula work orders, weighing and feeding records, emulsification or filling records, cleaning and sanitization records, semi-finished and finished product inspection reports, label audit forms, and others. Data sources include registered or filed formulas, raw material safety materials, weighing and emulsification equipment data, ERP/MES systems, laboratory test results, and packaging material version information. Data is updated per production batch or each line change. Fields include restricted substance content, production temperature, homogenization parameters, batch number, packaging material model, and others. Some fields must match the fixed format required by filing requirements.
Constraints imposed by these characteristics in the model integration and configuration link
Cosmetics manufacturing inspection and audit business is tightly bound to compliance requirements. Single-batch data volume is moderate but fields are highly specialized. Models must not cross batches in context and must strictly match filing rules. Access to multi-source heterogeneous data requires models to support tool calling, to read records from different systems such as ERP/MES and laboratories. Data is updated frequently per batch or line change. Configuration stability must be ensured to avoid frequent parameter adjustments that affect audit consistency. At the same time, the consequences of errors are directly linked to compliance-driven withdrawal or product recall. This requires model output conclusions to have clear document citation bases, so context and token limits must be adapted to the length of business documents.
How to set the configuration
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of documents related to single-batch production, avoiding context overflow that interrupts audit logic |
maxTokens | 4000–6000 token | Balances the completeness of audit conclusions and generation speed, meeting the processing needs of multiple document fragments |
quoteMaxToken | 1500–2000 token | Controls the length of document fragments referenced by the model, ensuring referenced content is precise and not redundant |
| Workflow/API tool selection | auto | Automatically determines whether to call tools to read production records, inspection data, and others, adapting to multi-source data audit scenarios |
temperature | 0.1–0.3 | Reduces generation randomness, ensuring the rigor of inspection and audit conclusions and meeting compliance requirements |
AIPROXY_API_TOKEN | Fill in the key allocated by the cloud service provider | Completes identity verification for large model calls, ensuring the platform can normally connect to large model services |
Three common mistakes
- Symptom: The available model list is empty, and model functions cannot be enabled or disabled. Cause:
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENis not configured correctly, preventing the platform from establishing a valid connection with the large model service. - Symptom: Some models cannot call preset tools, and the tool call judgment logic does not meet business expectations. Cause: The
toolChoiceparameter is set tonone, or the selected model does not match the supported tool call format. - Symptom: A
chat:ai_input_is_eerror is triggered when code running results are passed to the AI model in the workflow. Cause: The code output result has not been formatted cleaned, the content length exceeds themaxContextlimit, or contains special characters that cannot be recognized by the model.
How to confirm the configuration is complete
- Access the model management page of FastGPT v4.17.0, confirm that the configured
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENcan normally pull the available model list. - Create a test application for single-batch cosmetics production audit, submit a formula compliance audit request, and verify that the model can automatically call tools to read the feeding and inspection records of the corresponding batch.
- Submit a single inspection record audit task, check the length of the document fragment referenced in the model output result, and confirm that it matches the setting range of
quoteMaxToken. - Adjust the
temperatureparameter to around 0.2, submit multiple similar audit requests, and confirm that the consistency of the output results meets business audit requirements.
The values provided are common starting points and should be measured against the reader's own samples.